Method and system for effective channel estimation in a telecommunication system

ABSTRACT

A method and system is provided for estimating a first wireless communication channel (FWCC) transmitting a data stream from a first antenna system (FAS) to a second antenna system (SAS) in a telecommunication system, the telecommunication system having a second wireless communication channel (SWCC) for transmitting data from the MT to the antenna. First, one or more characteristics of the SWCC (SWCC Characteristics) are analyzed based on the data stream received by the antenna system from the MT. From the data stream received, an initial condition of the FWCC is also extracted. One or more characteristics of the FWCC (FWCC Characteristics) are predicted based on the SWCC Characteristics and the extracted initial condition, wherein analyzing the SWCC Characteristic and estimating the FWCC Characteristics relying on the feature that the data stream has a plurality of data segments whose Doppler frequencies are close to each other to an extent that the Doppler frequencies are deemed as the same for the purpose of analyzing the SWCC Characteristics.

[0001] This application is a continuation of U.S. application Ser. No.09/815,456, filed Mar. 23, 2001.

FIELD OF THE INVENTION

[0002] This invention relates generally to wireless communicationsystems having adaptive antenna arrays and more particularly to fullduplex adaptive antenna arrays in mobile communication systems.

BACKGROUND OF THE INVENTION

[0003] Smart antennas, or adaptive antenna arrays, are proved to havedistinct advantages in next generation wireless communication systems.The adaptive antenna array, usually deployed on a base station side ofthe wireless communication system, is capable of performing spatiallyselective communications (e.g., to transmit and receive) to optimize thesignal-to-interference-and-noise-ratio (SINR) at a desirable receivingterminal, leading to significant increase of power efficiency andchannel capacity on both the terminal-to-base-station (uplink) andbase-station-to-terminal (downlink) communications in the wirelesscommunication system. The basic concept of the adaptive antenna arraysystem used in a wireless communication system can be dissected andsummarized as the following four sequential steps:

[0004] (1) estimating the uplink wireless spatial signaturescorresponding to all the active terminals based on the data receivedfrom the antenna array;

[0005] (2) performing the uplink beamforming for the active terminalsbased on the uplink spatial signatures;

[0006] (3) estimating the downlink channel characteristics or downlinkspatial signatures; and

[0007] (4) performing downlink beamforming based on the downlink spatialsignatures.

[0008] The spatial signatures are also known or referred to as channelcharacteristics which represent a changing model of the communicationchannel involved.

[0009] For a time-division-duplex (TDD) communication system whichtransmits and receives information in the same carrier frequency butdifferent time slots, the downlink spatial signatures are identical tothe uplink spatial signatures if the terminal is fixed according to thereciprocity principle. However, for moving terminals, especially fastmoving terminals, the uplink spatial signatures and downlink spatialsignatures may not be the same due to the physical displacement of theterminal made between the uplink time slot and downlink time slot. As amatter of fact, a 5 ms separation in time can cause quite a significantchange of the spatial signatures if a terminal moves at a speed above 50miles per hour.

[0010] Therefore, in the case of dealing with a moving terminal, channelprediction techniques must be applied to predict downlink spatialsignatures based on the estimated uplink spatial signatures, knowingthat they will be different to a certain extent. Existing channelprediction techniques are only applicable to a single antenna system andno effective channel prediction algorithm has been proposed or known tohandle the channel prediction of an antenna array system. What is neededis a method for estimating the spatial signatures or characteristics ofmultiple channels in a telecommunication system.

[0011] This need for estimating the spatial signatures is also importantin a frequency-division-duplex (FDD) communication system. In such asystem, even if the terminal is fixed, the downlink spatial signaturesare significantly different from the uplink spatial signatures due tothe significantly different carrier frequencies used for both the uplinkand the downlink, except for a few exceptional scenarios (e.g., only onedirect path with no multipath). Also, it is deemed to be practicallyimpossible to derive the downlink spatial signatures from the uplinkspatial signatures. Therefore, to effectively implement a full duplexsmart antenna system, it is ideal to feedback the downlink spatialsignatures continuously from the terminal to the base station. Althoughit seems to be a good solution, it is hardly useful in a typical mobilecommunication system since this scheme requires too much overhead tofeedback the downlink spatial signatures to the base station especiallyif the downlink spatial signatures change rapidly due to a fast movingterminal.

[0012] What is needed is a feasible method and system for realizing thefull duplex adaptive antenna array for a telecommunication system.

SUMMARY

[0013] In one example of the present invention, a method is disclosedfor estimating a first wireless communication channel (FWCC)transmitting data from a first antenna system (FAS) to a second antennasystem (SAS) in a telecommunication system. The telecommunication systemhas a second wireless communication channel (SWCC) for transmitting datafrom the SAS to FAS. First, one or more characteristics of the SWCC(SWCC Characteristics) are analyzed based on a data stream received byFAS from the SAS. An initial condition of the FWCC is also extractedfrom the data received. One or more characteristics of the FWCC (FWCCCharacteristics) are then predicted based on the analyzed SWCCCharacteristics and the extracted initial condition of the FWCC.

[0014] In another example of the present invention, a method isdisclosed for estimating a first wireless communication channel(Downlink Channel) transmitting data from at least one antenna system toa communication terminal (CT) in a telecommunication system, thetelecommunication system having a second wireless communication channel(Uplink Channel) for transmitting data from the CT to the antennasystem. One or more characteristics of the Uplink Channel (UplinkChannel Characteristics) are analyzed based on a data stream received bythe antenna system from the CT, and an initial condition of the DownlinkChannel is also extracted from the data received. Based on the analyzedUplink Channel Characteristics and the extracted initial condition ofthe Downlink Channel, one or more characteristics of the DownlinkChannel (Downlink Channel Characteristics) are predicted.

[0015] In another example of the present invention, various channelestimation methods can be used for analyzing the Uplink ChannelCharacteristics and predicting the Downlink Channel Characteristicsrelying on the fact that the data stream has a plurality of datasegments whose Doppler frequencies are close to each other to the extentthat the Doppler frequencies are deemed as the same for the purpose ofanalyzing the Uplink Channel Characteristics.

[0016] In another example of the present invention, all theabove-described techniques are applied to a telecommunication systemwhose antenna system is an antenna array.

[0017] In yet another example of the present invention, all theabove-described techniques are applied to a telecommunication systemusing CDMA technologies or OFDM technologies.

[0018] The present invention can be applied to a telecommunicationsystem communicating with a mobile terminal as well as a fixed terminal.The present invention also enables full duplex adaptive antenna array inmobile communication systems to significantly increase system capacityand coverage, and mitigate or eliminate the fast fading effect indealing with fast moving mobiles.

BRIEF DESCRIPTION OF THE DRAWINGS

[0019]FIG. 1 shows an antenna array system and a mobile terminal in atypical mobile communication system with one direct path and a multipathcomponent between the antenna array and the mobile terminal.

[0020]FIG. 2 shows a flow diagram illustrating steps for implementingfull-duplex adaptive antenna array system in a mobile communicationsystem according to one example of the present invention.

[0021]FIG. 3 shows a detailed diagram of channel estimation procedureaccording one example of the present invention.

DETAILED DESCRIPTION OF THE INVENTION

[0022] Channel estimation usually involves multiple parameters, withsome of them are known to one side of the wireless communication systemsuch as the base station or the terminal. The final objective for thepresent invention is to assess and proximate the channel characteristicsbased on maximum numbers of known parameters and parameters derivabletherefrom.

[0023] Referring now to FIG. 1, in this example, an adaptive antennaarray subsystem (AAS) 100 is in wireless communication with an MT 50 atlocation 110 at a certain time instance. Note that the MT 50 at location110 may include in itself an adaptive antenna array or anomni-directional antenna or a directional antenna. As illustrated, thereare two communication paths from the middle antenna 102 of AAS 100 tothe MT 50 at location 110, one of which is a direct path 120 and theother path is a multipath which is reflected from a plane object 130 toarrive at the MT 50. Actually, the multipath 135 may be viewed as adirect path from an image 105 of AAS 100 with regard to the plane object130. This image 105 and the middle antenna AAS 100 are situatedsymmetrically on two sides of the reflection plane 130. For purposes ofillustration, it is assumed that in a few seconds, the MT 50 moves fromthe location marked with 110 to another location marked with 115.Corresponding direct path 120 and multipath 135 also move to paths 125and 140, respectively. Assuming that α₁ and α₂ are the angles betweenpaths 120 and 125 and the moving direction 145, respectively. It is alsoassumed that the length of the path from AAS 100 to the MT 50 atlocation 110 is L₁ and the length from AAS 100 to MT 50 at location 115is L₁+ΔL, where ΔL is the difference between path 125 and path 120 orthe path difference due to the motion. Accordingly, the relation amongthese parameters is defined by:

(L ₁ +ΔL ₁)² =L ₁ ²+(vτ)²−2L ₁ vτ cos α₁  (1)

[0024] where v is the moving speed of the MT 50.

[0025] Since in a typical wireless communication system, the length ofthe path from the middle antenna of AAS 100 to MT 50 at location 110 orMT 50 at location 115 is much larger than the displacement from thelocation 110 to location 115, i.e., L₁>>vτ. By ignoring the second ordereffect and further deriving equation (1), it is estimated that ΔL₁=−vτcos α₁. Using similar derivations on the multipath 135 and 140, asimilar equation is obtained: ΔL₁=−vτ cos α₂. The signal transmittedfrom the middle antenna of AAS 100 to MT 50 at location 110 is denotedas s(t)e^(j2Πft), where s(t) is the baseband signal and f is the carrierfrequency and t denotes time. Then the signal received by the MT 50 atlocation 115 will be s(t+τ+ΔL₁/c)e^(jΠf(t+) ^(+Δ) ^(L) ₁/c) =S(t+τ+τ cosα₁ v/c)e^(j2Πf(t+) ^(τ−τ) ^(cos) ^(α) ^(₁) v/c), where c is the wavetravel speed. For electromagnetic waves, c is the speed of light andv<<c in typical mobile communication scenarios. Therefore, the signalreceived by the MT 50 at location 115 can be well approximated bys(t+τ)e^(j2πƒ(t+τ))e^(−j2πƒτ cos α) ^(₁) ^(ν/c). The extra factore^(−j2πƒτ cos α) ^(₁) ^(ν/c) is caused by the motion also known as theDoppler effect and d₁=ƒ cos α₁ν/c is called the Doppler frequency. As itis shown, the Doppler frequency is a function of v and α₁.

[0026] A direct path 150 also exists between the first antenna 104 ofAAS 100 and the MT 50 at location 110. The angle between path 150 andmoving direction 145 is α₃=α₁+Δα. If it is assumed that the length ofpath 150 is L₃ and the distance between the first antenna and middleantenna 102 of AAS 100 is D. Therefore, Δα<sin⁻¹ D/L₃. In typicalwireless communication scenarios, D<<L₃, hence Δα can be approximated byzero, or in another words, α₃ and α₁ are almost the same. Following thesimilar argument, it is concluded that as long as the antenna array sizeis much smaller than the length of the communication path, the anglesbetween the path to the MT 50 and its moving direction can beapproximated to be the same for different antennas. Since the Dopplerfrequencies are determined by the aforementioned angles, all theantennas of an antenna array system are deemed to have approximately thesame set of Doppler frequencies.

[0027] For most commercial FDD cellular systems using frequencies in the800 MHz or 1800 MHz range, the frequency difference of the uplink anddownlink bands is about 5% of the carrier frequency. Therefore, theDoppler frequencies for uplink and downlink will be different by a smallpercentage (such as 5%) since the Doppler frequency is directlyproportionally to the carrier frequency. Furthermore, the downlinkDoppler frequency can also be easily derived from the uplink Dopplerfrequency since the exact uplink and downlink frequencies bear thefollowing relationship:

d ₁ =d ₂ f ₂ /f ₁,  (2)

[0028] where d₁ and d₂ are the uplink and downlink Doppler frequencies,respectively, and f₁ and f₂ are the uplink and downlink carrierfrequencies, respectively.

[0029] Now referring to FIG. 2, a method for implementing full-duplexantenna array communication systems according to one example of thepresent invention is shown. In the first step 200, the mobile (Doppler)channel is estimated based on the received data from multiple antennasor multiple codes (e.g., for CDMA or OFDM systems) in the uplinkchannel.

[0030] As described above, in a general scenario where there is an arrayof M antennas and a mobile terminal far away from the antenna array, thek-th channel of an antenna array can be expressed below, $\begin{matrix}{{{h_{k}(t)} = {\sum\limits_{i = 1}^{L}{\alpha_{k\quad i}^{{- {j2\pi}}\quad d_{i}t}{\delta \left( {t - \tau_{i}} \right)}}}},} & (3)\end{matrix}$

[0031] where L is the total number of multipath components, δ(t) theimpulse function, d₁, τ_(i) the Doppler frequency and time delay for thei-th multipath component, and α_(k1) the complex indicating theamplitude and phase shift of the i-th multipath component from themobile terminal to the k-th antenna of the antenna array. For anarrowband system where a signal coherent time is significantly smallerthan max{τ_(i)}, then the channel h_(k)(t) can be simplified as a scalarchannel below $\begin{matrix}{{h_{k}(t)} = {\sum\limits_{i = 1}^{L}{\alpha_{k\quad i}^{{- {j2\pi}}\quad d_{i}t}{{\delta (t)}.}}}} & (4)\end{matrix}$

[0032] Examples of the narrowband communication systems are the AdvancedMobile Phone System (AMPS) or IS-136 (narrowbandtime-division-duplex-system) where the signal bandwidth is around 30KHz. The narrowband concept can also be applied to the orthogonalfrequency division multiplex (OFDM) system where the aforementionedchannel characteristics is associated with one subcarrier or a smallgroup of neighboring subcarriers.

[0033] For a wideband system such as IS-95 code-division-multiple-access(CDMA) or wideband CDMA, where the signal coherent time is comparablewith the max{τ_(i)}, the above mentioned parameters can be grouped withthe similar τ_(i) and the channel can be expressed as follows,$\begin{matrix}{{{h_{k}(t)} = {{\sum\limits_{i = 1}^{G}{\left( {\sum\limits_{n = 1}^{L_{L\quad i}}{{a_{k\quad n}(i)}^{{- {j2\pi}}\quad {d_{n}{(i)}}t}}} \right){\delta \left( {t - \tau_{i}} \right)}}} = {\sum\limits_{i = 1}^{G}{{\beta_{k}(i)}{\delta \left( {t - \tau_{i}} \right)}}}}},} & (4)\end{matrix}$

[0034] where${\beta_{k}\left( {i,t} \right)} = {\sum\limits_{n = 1}^{L_{L\quad i}}{{a_{k\quad n}(i)}^{{- {j2\pi}}\quad {d_{n}{(i)}}t}}}$

[0035] represents a scalar channel model (or channel characteristics)corresponding to a particular delay group and G is the number of delaygroups for such a channel. In a CDMA system, β_(k)(i,t) corresponds to adespread value corresponding to the i-th chip delay.

[0036] For a narrowband system or each delay group of a wideband system,the scalar channel corresponding to each antenna can be simulated as asum of complex sinusoids whose frequencies d_(n)(i) are not the functionof k, and the complex amplitude α_(kn)(i) are different for eachantenna. In other words, although all the channels corresponding to anantenna array are different, they are formed by a linear combination ofthe same set of complex sinusoids. This is a powerful characteristicthat can be exploited to greatly enhance the accuracy of channelestimation mechanisms. For notational convenience, the index i or (i) inthe following derivations will be omitted, and the scalar channelcharacteristics are represented and expressed mathematically as follows,$\begin{matrix}{{{\beta_{k}(t)} = {\sum\limits_{n = 1}^{N}{a_{k\quad n}^{{- {j2\pi}}\quad d_{n}t}}}},} & (5)\end{matrix}$

[0037] where N is the number of the multiple components. Since β_(k)(t)is a sum of complex sinusoids and provided that the highest Dopplerfrequencies cannot exceed certain limit, (e.g., d_(n)=fv/c=267 Hz forf=2 GHz, v=100 mile/hour, and c=3×10⁸ m/s), the low-pass filter β_(k)(t)can be implemented with the cut-off frequency set at a point not muchlarger than the maximum Doppler frequencies. Consequently, a huge amountof noise and/ or other high frequency interference can be filtered outto enhance the quality of channel estimation. This implementation of alow-pass filter help significantly to extract the channelcharacteristics from the data received from uplink channelcommunications.

[0038] Many channel estimation methods (such as parametric estimationmethod or autoregressive method) known in the industry fail to, orneglect to, consider using uplink channel characteristics to estimatethe upcoming downlink characteristics due to the belief that uplinkchannel frequency will differ from the downlink channel frequency. Theyfail to recognize that a moving mobile terminal is likely to maintainits current motion for a certain period of time, and the informationfrom the uplink channel is a rich resource for predicting the channelcharacteristics for the downlink. None of the known methods for makingreasonable channel estimation exploits the aforementioned property thatall the channels associated with different antenna elements have thecommon Doppler frequencies although the channels themselves aredifferent. For the purposes of the present invention, it is understoodthat the Doppler frequencies are close to each other within apredetermined range, or to an extent that they have little or no effecton the mathematical equations applied. Therefore, the Dopplerfrequencies are deemed to be substantially the same. Using this feature,and taking Doppler channels or mobile channels into consideration, moredata is now available for estimating the downlink characteristics.

[0039] Given the model represented by formula (5) above, the well-knownparameter estimation method can be used to estimate d_(n) and α_(kn),given β_(k)(t) for k=1, . . . , K, where K is the number of antennas.However, the parameter K should not be restricted to the number ofantennas. For a single antenna system, K can be the number of modescorresponding to the same mobile terminal. For example, in a CDMAsystem, K can be the number of code channels and β_(k)(t) is thedemodulated symbols for the k-th code channel of the same carrierfrequency. In an OFDM system, K can be the number of closed spacedsubcarriers as long as their Doppler frequencies can be approximated asthe same and β_(k)(t) is the demodulated symbols for the k-thsubcarrier. Similarly, for a CDMA or OFDM antenna array system, K is themultiple of the number of antennas and number of code channels orsubcarriers, respectively. This also includes the case that the multiplecode channels in the CDMA system or the multiple OFDM subcarriers aretransmitted from different closely spaced antennas or a plurality ofclosely spaced antennas with different weight vectors at the terminal.As it can be understood by one ordinary skilled in the art, the abovesignals that share the same or similar Doppler frequencies are notlimited to just CDMA or OFDM signals. They can also be other types ofsignals such as FDMA or TDMA signals.

[0040] Fundamentally different from conventional channel estimationmethods as mentioned above, which do not exploit the fact that multipleantennas share the same common Doppler frequencies, the presentinvention takes into consideration this critical characteristic andextracts the needed information from more data then the conventionalmethod can while estimating the downlink channel characteristics.

[0041] For example, in the parameter estimation approach, since thed_(n) and α_(kn), can be estimated, the following cost function isminimized, $\begin{matrix}{{\sum\limits_{t = 0}^{T}{\sum\limits_{k = 1}^{K}{{{\beta_{k}(t)} - {\sum\limits_{n = 1}^{N}{a_{k\quad n}^{{- {j2\pi}}\quad d_{n}t}}}}}^{2}}},} & (6)\end{matrix}$

[0042] where t is assumed to be a discrete time index with a unitysampling rate.

[0043] According to the well-known autoregressive (AR) method (which isalso considered to be a particular method of the parameter estimationmethod), since β_(k)(t) is a sum of multiple exponentials, the followingequation holds, $\begin{matrix}{{{{\beta_{k}(t)} + {\sum\limits_{n = 1}^{N}{a_{n}{\beta_{k}\left( {t - n} \right)}}}} = 0},} & (7)\end{matrix}$

[0044] where {a_(n)} are a-coefficients and have the followingproperties, i.e., the polynomial formed by {a_(n)} $\begin{matrix}{{{z^{N} + {\sum\limits_{n = 1}^{N}{a_{n}z^{N - i}}}} = 0},} & (8)\end{matrix}$

[0045] has the roots of {_(e) ^(−j2πd) ^(_(n)) }, which are onlyfunctions of the Doppler frequencies {d_(n)}. In this case, it is clearthat the channel characteristics also depend on the historical dataβ_(k)(t−n).

[0046] Therefore, given {β₁(t), . . . , β_(M)(t)}_(T) ⁼⁰, the followingmatrix equation can be formed:

B _(k)(N) α =0,  (9)

[0047] where $\begin{matrix}{{{B_{k}(N)} = \begin{bmatrix}{\beta_{k}(N)} & {\beta_{k}\left( {N - 1} \right)} & \ldots & {\beta_{k}(0)} \\{\beta_{k}\left( {N + 1} \right)} & {\beta_{k}(N)} & \ldots & {\beta_{k}(1)} \\\vdots & \vdots & ⋰ & \vdots \\{\beta_{k}(T)} & {\beta_{k}\left( {T - 1} \right)} & \ldots & {\beta_{k}\left( {T - N} \right)}\end{bmatrix}},{\underset{\_}{a} = {\begin{bmatrix}1 \\a_{1} \\\vdots \\a_{N}\end{bmatrix}.}}} & (10)\end{matrix}$

[0048] It is noted that by using the antenna array, more data isavailable here for analysis purposes. Each β_(k) represents an antennasubsystem contained in the antenna array, and for each antennasubsystem, β_(k)(0) to β_(k)(T−N) represents various data points. It isunderstood that in a multiple access telecommunication system such as aCDMA system, B_(k) can also be constructed by using β_(k) to representkth data segment (or data mode). Since all {β₁(t), . . . , β_(M)(t)}share the same coefficients {a_(n)}, the following equation also holds,$\begin{matrix}{{{{B(N)}\underset{\_}{a}} = 0},{{B(N)} = {\begin{bmatrix}{B_{1}(N)} \\\ldots \\{B_{M}(N)}\end{bmatrix}.}}} & (11)\end{matrix}$

[0049] Consequently, a=[1, a₁, . . . , a_(M)] can be estimated fromsolving the above equations.

[0050] By cascading B₁(N) to B_(M)(N) (i.e., integrating various antennasubsystems together), more equations or more constraints can be imposedon the a-coefficients, hence, the performance of the estimation can besignificantly enhanced.

[0051] It is worth noting that β_(k)(t) is a sum of complex sinusoids,and the following recursive equation also holds: $\begin{matrix}{{{\overset{\_}{\beta_{k}(t)} + {\sum\limits_{n = 1}^{N}{a_{n}\overset{\_}{\beta_{k}\left( {t + n} \right)}}}} = 0},} & (12)\end{matrix}$

[0052] where {overscore ((•))} indicates complex conjugate. This is alsoknown as the forward and backward smoothing technique. Therefore, theperformance of the estimation can be further improved by stacking twiceas many equations on the a-coefficients, i.e., C(N)α=0, where${{C(N)} = \begin{bmatrix}{B(N)} \\{B^{\prime}(N)}\end{bmatrix}},{{B^{\prime}(N)} = \begin{bmatrix}{B_{1}^{\prime}(N)} \\\ldots \\{B_{M}^{\prime}(N)}\end{bmatrix}},{a\quad n\quad d}$${B_{k}^{\prime}(N)} = {\begin{bmatrix}\overset{\_}{\beta_{k}(0)} & \overset{\_}{\beta_{k}(1)} & \ldots & \overset{\_}{\beta_{k}(N)} \\\overset{\_}{\beta_{k}(1)} & \overset{\_}{\beta_{k}(2)} & \ldots & \overset{\_}{\beta_{k}\left( {N + 1} \right)} \\\vdots & \vdots & ⋰ & \vdots \\\overset{\_}{\beta_{k}\left( {T - N} \right)} & \overset{\_}{\beta_{k}\left( {T - N + 1} \right)} & \ldots & \overset{\_}{\beta_{k}(T)}\end{bmatrix}.}$

[0053] By stacking B(N) and B′(N), more equations are created forproducing a better estimation of a-coefficients.

[0054] One well-known method for estimating the values of a while givenB(N) if either B(N) or β_(k)(t) is noise contaminated is the leastsquares method. Another known method is to perform singular valuedecomposition on B(N). In this method, the estimate of a can be obtainedby selecting the right singular vector corresponding to the smallestsingular value and performing proper scaling to make its first elementequal to one.

[0055] With {a_(n) ^(_(n)) } in place, the exponentials {_(e) ^(−j2πd)}can be found by rooting the polynomial as in equation (8). After findingthose exponentials, the least squares method can be used to find{α_(kn)} as shown below:

α _(k)=(E ^(*) *E)⁻¹ E ^(*) β _(k),  (13)

[0056] where ${E = \begin{bmatrix}1 & ^{{- {j2\pi}}\quad d_{1}} & \cdots & ^{{- {j2\pi}}\quad d_{n}T} \\1 & ^{{- {j2\pi}}\quad d_{2}} & \cdots & ^{{- {j2\pi}}\quad d_{2}T} \\\vdots & \vdots & ⋰ & \vdots \\1 & ^{{- {j2\pi}}\quad d_{N}} & \cdots & ^{{- {j2\pi}}\quad d_{N}T}\end{bmatrix}},{{\underset{\_}{\beta}}_{k} = \begin{bmatrix}{\beta_{k}(0)} \\{\beta_{k}(1)} \\\vdots \\{\beta_{k}(T)}\end{bmatrix}},{{\underset{\_}{\alpha}}_{k} = \begin{bmatrix}1 \\{\hat{\alpha}}_{k1} \\\vdots \\{\hat{\alpha}}_{k\quad N}\end{bmatrix}},$

[0057] ( )^(*) denotes conjugate transpose and {circle over (α)}_(kn) isthe least squares estimate of α_(kn).

[0058] In the above-described method, the estimation errors of theexponentials of the Doppler frequencies are not taken intoconsideration. If such an estimation error of {_(e) ^(−t2πd) ^(_(n)) }can be properly estimated, so does the estimation error variance σ orE^(*)E in formula (12) can be replaced by E^(*)E+σI, where I is theidentity matrix with dimension of N.

[0059] The next task is to estimate N based upon the β_(k)(t). Oneexample of the present invention provides a method of estimating N byforming the following matrix with p: $\begin{matrix}{{{B(p)} = \begin{bmatrix}{B_{1}(p)} \\\vdots \\{B_{M}(p)}\end{bmatrix}}{w\quad h\quad e\quad r\quad e}} & (14) \\{{B_{k}(p)} = {\begin{bmatrix}{\beta_{k}(p)} & {\beta_{k}\left( {p - 1} \right)} & \cdots & {\beta_{k}(0)} \\{\beta_{k}\left( {p + 1} \right)} & {\beta_{k}(p)} & \cdots & {\beta_{k}(1)} \\\vdots & \vdots & ⋰ & \vdots \\{\beta_{k}(T)} & {\beta_{k}\left( {T - 1} \right)} & \cdots & {\beta_{k}\left( {T - p} \right)}\end{bmatrix}.}} & (15)\end{matrix}$

[0060] With the above matrix, the rank of B(p) can be detected. If therank of B(p) is p and T is far bigger than p, then p is furtherincreased until B(p) becomes close to rank deficient and N=rank(B(p))−1.

[0061] Another method to estimate N can be implemented based oninformation theoretic criteria as described in detail by Mati Wax inOrder Selection for AR Models by Predictive Least Square, IEEETransactions on Acoustics, Speech and Signal Processing, Vol. 36, No. 4,April, 1988, which is incorporated by reference in its entirety.

[0062] Referring back to FIG. 2, in step 210, beamforming is performedfor the uplink based on the received data and the analyzed uplinkchannel characteristics of all co-channel signals. One effective methodto enhance the uplink beamforming is to find the time varying unit-normbeamforming vectors${{\underset{\_}{\gamma}}^{(i)}(t)} = \begin{bmatrix}{\gamma_{1}^{(i)}(t)} \\\vdots \\{\gamma_{M}^{(i)}(t)}\end{bmatrix}$

[0063] to maximize the signal-to-interference ratio $\begin{matrix}{{{S\quad I\quad N\quad R^{(i)}} = \frac{\left| {{{\underset{\_}{\gamma}}^{(i)}(t)}^{*}{{\underset{\_}{\beta}}^{(i)}(t)}} \right|^{2}}{\sum\limits_{{p = 1},{p \neq i}}^{L}\left| {{{\underset{\_}{\gamma}}^{(i)}(t)}^{*}{{\underset{\_}{\beta}}^{(p)}(t)}} \middle| {}_{2}{+ \left| {{{\underset{\_}{\gamma}}^{(i)}(t)}^{*}{\underset{\_}{n}(t)}} \right|^{2}} \right.}},} & (16)\end{matrix}$

[0064] where L is the total number of co-channel signals, β ^((i))(t) isthe channel characteristic corresponding to the i-th terminal, and n(t)is the combination of the thermal noise and other co-channelinterference, e.g., from other cells.

[0065] Another method for enhancing the uplink beamforming is tomaximize the signal strength, i.e., γ ^((i))(t)={overscore(β)}^((i))(t), where {overscore ((•))} indicates complex conjugate. Withγ ^((i))(t) and the data vector x(t), the uplink beamforming can berepresented as below,

y ^((i))(t)= γ ^((i))(t)^(*) x (t).  (17)

[0066] It is understood that with the channel characteristics andreceive data vectors, other similar uplink beamforming methods wellknown in the industry can be used as well.

[0067] In step 220 of FIG. 2, the downlink channel characteristics needto be predicted based on the obtained uplink channel characteristics.The estimation of the downlink channel characteristics uses roughly thesame techniques as those described above with regard to analyzing theuplink channel characteristics with the exception that the parametersneeds to be estimated are reversed. For instance, taking equation (7)for example, in analyzing the uplink channel characteristics, β_(k)(t)is known, and the parameter need to be approximated is thea-coefficients, while for predicting the downlink channelcharacteristics, the a-coefficients are presumed to be known from theanalyzed uplink channel, and the β_(k)(t) is sought instead. It is notedthat for the purpose of the present invention, the a-coefficients of theuplink channel and the a-coefficients of the downlink channel are closeto each other to the extent that their difference has no or littleimpact on mathematical derivations of other parameters in assisting theprediction of the downlink characteristics.

[0068] According to one example of the present invention, for TDDsystems, since both uplink and downlink share the identical carrierfrequency, they share the identical channel characteristics except forthe correction of the receive calibration vector c, i.e., ρ^((i))(t)=c{circle over (x)}β ^((t))(t), where {circle over (x)} denoteselement wise product of two vectors. The k-th element of the vector c,c_(k), is a ratio of the transmit complex gain (including phase offset)g_(k) ^((t)) and receive complex gain g_(k) ^((r)) of the k-thtransceiver, i.e., c_(k)=g_(k) ^((t))/g_(k) ^((r)).

[0069] In order to accurately estimate the downlink channelcharacteristics, it is important to set or predict certain initialcondition of the downlink channel. One example of the present inventionuses corrected T samples of uplink channel responses {ρ^((i))(t)=c{circle over (x)}β ^((i))(t)}_(t=T) _(d) _(−T) ^(T) ^(_(d))⁻¹ immediately before the downlink start time T_(d) as the downlinkchannel's initial conditions from time to time. With the estimatedchannel parameters {_(e) ^(−j2πd) ^(_(n)) }, {α_(kn)} can then bepredicted as in formula (12), where T≧N. Further, the downlink channelcharacteristics {ρ_(k)(t)} can be estimated by plugging in the estimated{α_(kn,e) ^(−j2πd) ^(_(n)) } parameters to formula (6) for t>T_(d)−1.

[0070] Another method for predicting the downlink channelcharacteristics is to use the recursive equation of (7), $\begin{matrix}{{{\rho_{k}(t)} = {- {\sum\limits_{n = 1}^{N}{a_{n}{\rho_{k}\left( {t - n} \right)}}}}},} & (18)\end{matrix}$

[0071] given its history ρ_(k)(t−n), the constant a-coefficients{a_(n)}, and functions of N Doppler frequencies {d_(n)}. In theimmediate example, the channel prediction can be performed for t24 T_(d)given only the a-coefficients {a_(n)} and the initial conditions of thechannel characteristics {ρ_(k)(T_(d)−1), . . . , ρ_(k)(T_(d)−T)}.

[0072] For FDD systems where the uplink and downlink carrier frequenciesare different, the Doppler frequencies for the uplink can first be foundand the downlink Doppler frequencies are then estimated according toequation (2). In order to find the exponentials {_(e) ^(−j2πd) ^(_(n)) }and the downlink a-coefficients, the following steps are contemplated:

[0073] 1. expanding the polynomial${{P(z)} = {\prod\limits_{n = 1}^{N}\quad \left( {z - ^{{- {j2\pi}}\quad d_{n}}} \right)}};$

[0074] 2. using the a-coefficient of P(z) corresponding to z^(N-n) forthe a-coefficients a_(n).

[0075] For PCS and cellular systems where the frequency difference iswithin 5% of the carrier frequency, the above calculation can beeliminated, and the uplink a-coefficients is used instead to approximatethe downlink a-coefficients.

[0076] With the exponentials or a-coefficients {a_(n)} collected(referred to as channel parameters), it may still be hard to performchannel prediction since the initial conditions of the downlink channelcharacteristics (i.e., ρ ^((i))(T_(d)−T), ρ ^((i))(T_(d)−T+1), . . . , ρ^((i))(T_(d)−1), T≧T_(d)) remain unclear. In one example of the presentinvention, a feedback method is employed to feedback the downlinkchannel characteristics from the mobile terminals periodically,therefore providing a close-to-real initial condition at that timeinstance.

[0077] Referring now to FIG. 3, detailed procedures for using thefeedback method for providing initial conditions of the downlink channelperiodically is shown.

[0078] Execution begins in step 300, where the AAS transmits multipletraining signals known and discernable to the MT. In one example, Msignals are transmitted from the M antennas of the AAS. Based on thetraining signals transmitted, the downlink channel characteristics arefound in step 310. For instance, the i-th MT receives all these trainingsignals and can easily estimate the channel characteristicscorresponding to all the M antennas {ρ ^((t))(T_(d)−T), ρ^((i))(T_(d)−T+1), . . . , ρ ^((i))(T_(d)−1)} predicated on the sum ofthese signals. One way to make the training signal distinctive is tomake all the antennas of the AAS transmit M mutually orthogonal signals.If the k-th signal or code is used at the MT to despread the receivedtraining signal, its component corresponding to the k-th antenna ortransmitter can be readily extracted.

[0079] In another example of the present invention, the same trainingsignal can be scheduled to be transmitted from different antennas indifferent time slots so that the i-th MT can easily separate andidentify the training signals (or downlink channel characteristics) fromdifferent antennas or transmitters.

[0080] Further in step 320 of FIG. 3, the i-th MT modulates the Tsamples of the downlink channel characteristics {ρ ^((i))(T_(d)−T), ρ^((t))(T_(d)−T+1), . . . , ρ ^((i))(T_(d)−1)} and transmits them back tothe AAS in the next available time slot designated for the feedbacksignals. In step 330, the AAS receives the downlink channelcharacteristics and use them as initial conditions for future channelprediction. The AAS knows the exact transmit time of the trainingsignals and the receiving time of the feedback signal, and it ensuresthat the feedback signals are appropriately applied for the initialconditions. Of course, the AAS can also use {ρ ^((i))(T_(d)−T), ρ^((i))(T_(d)−T+1), . . . , ρ ^((i))(T_(d)−1)} along with the uplink datato estimate the downlink channel parameters since there are many moredata samples from the uplink communications. Henceforth, with {ρ^((i))(T_(d)−T), ρ ^((i))(T_(d)−T+1), . . . , ρ ^((i))(T_(d)−1)} and thedownlink channel parameters, the same aforementioned methods as in theTDD systems can be used to predict the downlink channel characteristics.Clearly, the use of feedback signals are universally applicable in orderto enhance the accuracy of the channel estimation.

[0081] Referring again to FIG. 2, with the predicted downlink channelcharacteristics of all the MTs, in step 230, the downlink beamforming isperformed to maximize the strength or the SINR of the signal at thedesired MT. In one example, a unit-norm downlink beamforming vector η^((i))(t) is found such that $\begin{matrix}{{{S\quad I\quad N\quad R^{(i)}} = \frac{\left| {{{\underset{\_}{\eta}}^{(i)}(t)}^{*}{{\underset{\_}{\rho}}^{(i)}(t)}} \right|^{2}}{\sum\limits_{{p = 1},{p \neq i}}^{L}\left| {{{\underset{\_}{\eta}}^{(p)}(t)}^{*}{{\underset{\_}{\rho}}^{(i)}(t)}} \right|^{2}}},} & (19)\end{matrix}$

[0082] is maximized, where η ^((i))(t) and ρ ^((t))(t) are the downlinkbeamforming vectors and channel characteristics for the i-th terminal.

[0083] Another approach for performing downlink beamforming is tomaximize the signal strength at the i-th terminal, i.e., η^((i))(t)={overscore (ρ)}^((i))(t). In this case, the downlinkbeamforming is performed as follows, $\begin{matrix}{{{\underset{\_}{z}(t)} = {\sum\limits_{i = 1}^{L}{{{\underset{\_}{\eta}}^{(i)}(t)}{s^{(i)}(t)}}}},} & (20)\end{matrix}$

[0084] where L is the number of co-channel terminals and s^((i))(t) isthe signal for the i-th MT.

[0085] For illustration purposes, the above disclosure of the presentinvention is explained in the context of using an adaptive antenna arraystation in a telecommunication system. However, as mentioned in variousplaces, it is understood that the techniques disclosed can be equallyapplied to a single antenna system, especially for telecommunicationsystems using OFDM and CDMA technologies, in which a group ofsubcarriers or code channels respectively can provide data from multiplechannels to the antenna.

[0086] In addition, the above-disclosed examples of the presentinvention specifically explore the scenario of communicating with amobile terminal. It is also understood, various analyses for the uplinkchannel characteristics and estimation for the downlink channel can beeasily applied to deal with a fixed terminal due to the need thatcertain changes of its surrounding environment may very likely causecorresponding variations of its channel characteristics.

[0087] Thus, what has been illustrated above is a new effective methodto implement the full-duplex adaptive antenna array systems for mobilecommunications. By effectively estimating both the uplink and downlinkchannel characteristics, the downlink beamforming process issignificantly enhanced. It is understood that the same type ofintelligence can be used in a reversed manner. That is, instead ofhaving the base stations predict the downlink characteristics based onthe analyzed and estimated uplink characteristics, the communicationterminal such as the cell phone can also estimate and analyze thedownlink characteristics in order to predict future uplinkcharacteristics.

[0088] The above disclosure provides many different embodiments, orexamples, for implementing different features of the invention. Also,specific examples of components, and processes are described to helpclarify the invention. These are, of course, merely examples and are notintended to limit the invention from that described in the claims.

[0089] While the invention has been particularly shown and describedwith reference to the preferred embodiment thereof, it will beunderstood by those skilled in the art that various changes in form anddetail may be made therein without departing from the spirit and scopeof the invention.

What is claimed is:
 1. A method for estimating a first wirelesscommunication channel (FWCC) transmitting data from a first antennasystem (FAS) to a second antenna system (SAS) in a telecommunicationsystem, the telecommunication system having a second wirelesscommunication channel (SWCC) for transmitting data from the SAS to FAS,the method comprising: analyzing one or more characteristics of the SWCC(SWCC Characteristics) based on a data stream received by FAS from theSAS; extracting an initial condition of the FWCC from the data received;and predicting one or more characteristics of the FWCC (FWCCCharacteristics) based on the analyzed SWCC Characteristics and theextracted initial condition of the FWCC.
 2. The method of claim 1wherein the SAS is a part of a moving mobile terminal.
 3. The method ofclaim 1 wherein the steps of analyzing and predicting use a plurality ofdata segments of the data stream whose Doppler frequencies are deemed assubstantially the same for the purpose of analyzing the SWCCCharacteristics.
 4. The method of claim 3 wherein each data segment isreceived by a single antenna subsystem if FAS uses an antenna array. 5.The method of claim 3 wherein each data segment is received through acode channel if the telecommunication system uses CDMA technologies. 6.The method of claim 3 wherein each data segment is received from one ofa plurality of antennas of the SAS.
 7. The method of claim 3 whereineach data segment is received from one beamformed version of an antennaarray of the SAS.
 8. The method of claim 3 wherein each data segment isreceived through a sub-carrier frequency if the telecommunication systemuses OFDM technologies.
 9. The method of claim 1 wherein the step ofanalyzing further comprises using an autoregressive method incorporatingat least one predetermined data matrices formed by a plurality of datasegments.
 10. The method of claim 1 wherein the data stream contains aresponse signal stream sent by the SAS immediately before transmittingdata through the FWCC responding to at least one prior training signal.11. The method of claim 1 wherein the step of analyzing furthercomprises implementing a low-pass filter for extracting the SWCCCharacteristics without being interfered by noises of high frequencies.12. The method of claim 1 wherein the step of analyzing furthercomprises using a parametric estimation method taking into considerationof one or more Doppler frequencies of the received data stream.
 13. Themethod of claim 12 wherein the step of analyzing further comprisesforward and backward smoothing to enhance analyzing the SWCCCharacteristics, which further enhances the prediction of the FWCCCharacteristics.
 14. The method of claim 1 wherein the step ofextracting further comprises using the SWCC to estimate the initialcondition of the FWCC.
 15. The method of claim 1 wherein the step ofpredicting further comprises estimating a Doppler frequency of the FWCCfrom a corresponding Doppler frequency of the SWCC based on carrierfrequencies for the FWCC and the SWCC.
 16. The method of claim 1 whereinthe step of predicting further comprises using a-coefficients of theSWCC as the a-coefficients of the FWCC when carrier frequencies of theSWCC and the FWCC are deemed to be substantially the same for thepurpose of predicting the FWCC Characteristics.
 17. The method of claim1 further comprising performing beamforming for the FWCC by finding timevarying unit-norm beamforming vectors to maximize a signal tointerference ratio.
 18. The method of claim 1 further comprisingperforming beamforming for the SWCC based on the predicted SWCCCharacteristics.
 19. A method for channel estimation for atelecommunication system using an antenna array, the antenna arraycontaining a plurality of antenna subsystems, the method comprising:analyzing one or more characteristics of a wireless communication uplinkchannel (Uplink Channel Characteristics) based on data received by allantenna subsystems from a mobile terminal (MT); extracting informationfor an initial condition of a wireless communication downlink channel;and predicting one or more characteristics the wireless communicationdownlink channel (Downlink Channel Characteristics) based on the UplinkChannel Characteristics and the extracted initial condition of thedownlink channel, wherein the wireless communication uplink channeltransmits data from the MT to the antenna array, and the wirelesscommunication downlink channel transmits data from the antenna array tothe MT, and wherein the step of analyzing and predicting utilizing acommon feature that a Doppler frequency of each mobile channelestablished between an antenna subsystem and the MT is close to that ofother mobile channels between other antenna subsystems and the MT to theextent that all the Doppler frequencies are deemed as the samefrequency.
 20. The method of claim 19 wherein the steps of analyzingfurther comprise the step of implementing a low-pass filter forextracting the Uplink Channel Characteristics without being interferedby noises and undesired signals of high frequencies.
 21. The method ofclaim 19 wherein the step of analyzing further comprises analyzing themobile channels based on the data received.
 22. The method of claim 19wherein the step of extracting further comprises the steps of: providinga training signal periodically from each antenna subsystem to the MT;deriving the Downlink Channel Characteristics by the MT based on thetraining signals; and transmitting from the MT to the antenna array thederived Downlink Channel Characteristics.
 23. The method of claim 22wherein each training signal is orthogonal to the others.
 24. The methodof claim 19 wherein the step of analyzing further comprises using aparameter estimation method integrated with forward and backwardsmoothing mechanisms.
 25. The method of claim 19 wherein the step ofanalyzing further comprises using a parameter estimation methodconsidering that all the Doppler frequencies are deemed as substantiallythe same.
 26. The method of claim 19 wherein the step of analyzingfurther comprises using an autoregressive method incorporating at leastone predetermined data matrices formed by a plurality of data segments.27. The method of claim 19 wherein the step of extracting furthercomprises using the uplink channel to estimate the initial condition ofthe downlink channel.
 28. The method of claim 19 wherein the step ofpredicting further comprises estimating a Doppler frequency of thedownlink channel from a corresponding Doppler frequency of the uplinkchannel based on carrier frequencies for the downlink channel and theuplink channel.
 29. The method of claim 19 wherein the step ofpredicting further comprises using a-coefficients of the uplink channelas the a-coefficients of the downlink channel when carrier frequenciesof the uplink channel and the downlink channel are close to each otherto an extent that they are deemed as the same for the purpose ofpredicting the Downlink Channel Characteristics.
 30. The method of claim19 further comprising performing beamforming for the downlink channelbased on the predicted Downlink Channel Characteristics.
 31. Anintelligent antenna system for estimating a first wireless communicationchannel (FWCC) transmitting data from a first antenna system (FAS) to asecond antenna system (SAS) in a telecommunication system, thetelecommunication system having a second wireless communication channel(SWCC) for transmitting data from the SAS to FAS, the system comprising:means for analyzing one or more characteristics of the SWCC (SWCCCharacteristics) based on a data stream received by FAS from the SAS;means for extracting an initial condition of the FWCC from the datareceived; and mean for predicting one or more characteristics of theFWCC (FWCC Characteristics) based on the analyzed SWCC Characteristicsand the extracted initial condition of the FWCC.
 32. The system of claim31 wherein the SAS is a part of a moving mobile terminal.
 33. The systemof claim 31 wherein the means for analyzing uses a plurality of datasegments of the data stream whose Doppler frequencies are deemed assubstantially the same for the purpose of analyzing the SWCCCharacteristics.
 34. The system of claim 33 wherein each data segment isreceived by a single antenna subsystem if FAS uses an antenna array. 35.The system of claim 33 wherein each data segment is received through acode channel if the telecommunication system uses CDMA technologies. 36.The system of claim 33 wherein each data segment is received through asub-carrier frequency if the telecommunication system uses OFDMtechnologies.
 37. The system of claim 31 wherein the data streamcontains a response signal stream sent by the SAS immediately beforetransmitting data through the FWCC responding to at least one priortraining signal.
 38. The system of claim 31 wherein the means foranalyzing further comprises a low-pass filter implemented for extractingthe SWCC Characteristics without being interfered by noises.
 39. Thesystem of claim 31 wherein the means for analyzing further comprisesmeans for using a parametric estimation method taking into considerationof one or more Doppler frequencies of the received data stream.
 40. Thesystem of claim 39 wherein the means for analyzing further comprisesmeans for forward and backward smoothing to enhance analyzing the SWCCCharacteristics and predicting the FWCC Characteristics.
 41. The systemof claim 31 wherein the means for analyzing further comprises using anautoregressive method incorporating at least one predetermined datamatrices formed by a plurality of data segments.
 42. The method of claim31 wherein the means for extracting further comprises using the SWCC toestimate the initial condition of the FWCC.
 43. The system of claim 31wherein the means for predicting further comprises means for estimatinga Doppler frequency of the FWCC from a corresponding Doppler frequencyof the SWCC based on carrier frequencies for the FWCC and the SWCC. 44.The system of claim 31 wherein the means for predicting furthercomprises means for using a-coefficients of the SWCC as thea-coefficients of the FWCC when carrier frequencies of the SWCC and theFWCC are close to each other within a predetermined range that they aredeemed as substantially the same for the purpose of predicting the FWCCCharacteristics.